Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

publishedVersio

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.publishedVersio

A novel optimization algorithm based PID controller design for real-time optimization of cutting depth and surface roughness in finish hard turning processes

This paper proposes a novel method to improve surface finish in turning processes by effectively controlling the cutting depth. A metaheuristic algorithm based PID control system, in combination with a piezoelectric vibration sensor for feedback, is introduced to regulate the position of the servo motor that controls the cutting tool. The PID controller is optimized using Q-learning based Sand Cat Optimization algorithm to achieve the best performance in terms of cutting depth accuracy and surface finish quality. The piezoelectric sensor provides real-time feedback information about the cutting process and allows for precise adjustments to the cutting depth. The results demonstrate the proposed system's ability to handle variations in cutting conditions and tool's wear and tear. Compared to highly optimized standard PID control, improved robustness and stability has been achieved in experimental results by proposed framework. Experimental results demonstrate improved robustness and stability compared to standard PID control. Several materials with high hardness of 20–65 HRC including Phenolic Bakelite, Copper, Thermoplastic, and Stainless Steel (AISI-420, AA6061-T6, and AISI-316) are tested. Minimum value of Vibration amplitude achieved is 0.598 μm for cutting depth of 0.25 mm. The sustained minimum amplitude of vibration and Ra value of the surface finish is found comparable to standard models with less than 10% error

Hybrid General Regression NN Model for Efficient Operation of Centralized TEG System under Non-Uniform Thermal Gradients

The global energy demand, along with the proportionate share of renewable energy, is increasing rapidly. Renewables such as thermoelectric generators (TEG) systems have lower power ratings but a highly durable and cost-effective renewable energy technology that can deal with waste heat energy. The main issues associated with TEG systems are related to their vigorous operating conditions. The dynamic temperature gradient across TEG surfaces induces non-uniform temperature distribution (NUTD) that significantly impacts the available output electrical energy. The mismatching current impact may lower the energy yield by up to 70% of extractable thermal energy. As a solution, a hybrid general regression neural network (GRNN) orca predation algorithm (OPA) is proposed; backpropagation limitations are minimized by utilizing the stochastic optimization algorithm named OPA. The conclusions are evaluated and contrasted with highly improved versions of the conventional particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawk optimization (HHO). A detailed analytical and statistical analysis is carried out through five distinct case studies, including field stochastic data study, NUTD, varying temperature, and load studies. Along with statistical matrix errors such as MAE, RMSE, and RE, the results are assessed in terms of efficiency, tracking, and settling time. The results show that superior performance is achieved by the proposed GRNN-OPA based MPPT by 35% faster tracking, and up to 90–110% quicker settling time which, in turn, enables the 4–8% higher energy accumulation over a longer period of operation. A low-cost experimental setup is devised to further validate the practicality of the proposed techniques. From such comprehensive analysis, it can be safely concluded that the proposed GRNN-OPA successfully undertakes the drawbacks of existing classical MPPT methods with higher efficiency

Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique

Energy harvesting and stability analysis of centralized TEG system under non-uniform temperature distribution

Thermoelectric generator (TEG) systems are gaining much attraction due to utility in heat recovery, surface cooling, concentrated solar thermal, and sensor applications. In series–parallel configurations, TEG modules due to Heterogeneous temperature difference (HeTD) show the non-linear behavior. Due to non-uniform temperature distribution (NUTD), multiple maximum power points (MPP) appears on the P-V curve. It is crucial to drive the system at true global maximum power point (GMPP) among multiple MPP's. Existing classical techniques exhibit slow tracking, low efficiency, and undesired fluctuation in output voltage transients. To address these shortcomings our control technique based on improved Moth Flame Optimization (IMFO) is employed for the maximum power point tracking (MPPT) control under dynamic operating conditions. Comparison of the proposed technique is made with other well-known meta-heuristic techniques including particle swarm optimization (PSO), cuckoo search (CS), Artificial Bee Colony (ABC), and recently developed Dragon Fly Optimization (DFO). The comprehensive case studies with statistical and quantitative analysis are performed to confirm the superior performance of IMFO for NUTD condition, fast varying temperature condition, and stochastic operations. To experimentally validate the performance of the IMFO algorithm a low-cost TEG emulator setup is designed. The IMFO based control is implemented on a low-cost microcontroller achieving effective real-time control application in hardware. The proposed IMFO algorithm attains up to 6 W more power and takes 59% less time to track and settle at GMPP with minimum fluctuation. Results also validate that IMFO extracts 5.2% more electrical energy in comparison to competing techniques. In light of comprehensive analysis, it is safe to conclude that the proposed IMFO performs excellently for TEG MPPT control

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically

Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys

Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris’ law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordance with neural networks to accurately forecast the crack growth rates in aluminum alloys. Through experimental data, the performance of the hybrid metaheuristic optimization–neural networks has been tested. A dynamic Levy flight function has been incorporated with a chimp optimization algorithm to accurately train the deep neural network. The performance of the proposed predictive model has been tested using 7055 T7511 and 6013 T651 alloys against four competing techniques. Results show the proposed predictive model achieves lower correlation error, least relative error, mean absolute error, and root mean square error values while shortening the run time by 11.28%. It is evident through experimental study and statistical analysis that the crack length and growth rates are predicted with high fidelity and very high resolution

A Novel MPPT Controller Based on Mud Ring Optimization Algorithm for Centralized Thermoelectric Generator under Dynamic Thermal Gradients

Most industrial processes generate raw heat. To enhance the efficiency of industrial operations, this raw heat is recovered. Thermoelectric generators (TEG), as solid state devices, provide an excellent application of heat recovery in the form of most manageable electrical power. This work presents a novel MPPT controller based on the Mud Ring Optimization algorithm for a centralized Thermoelectric Generator (TEG) under dynamic thermal gradients. The existing stochastic optimization algorithm for Maximum Power Point Tracking (MPPT) control in renewable energy systems exhibits several limitations that affect its performance in MPPT control. The convergence speed, local minima trap, hyper parameters’ sensitivity toward the population size, acceleration coefficients, and the stopping criterion all impact the convergence stability. In addition to these limitations, sensor noise sensitivity in measurement fluctuates the control system leading to reduced performance. Therefore, the careful design and implementation of the MRO algorithm is crucial to overcome these limitations and achieve a satisfactory performance in MPPT control. The results of this study contribute to developing more efficient MPPT control of TEG systems and implementing renewable energy technologies. The algorithm effectively tracks the maximum power point in dynamic thermal environments and increases the power output compared to conventional MPPT methods. The findings illustrate the efficacy of the proposed controller providing a higher power output (Avg. 99.95%) and faster response time (220 ms) under dynamic thermal conditions achieving 38–70% faster tracking of the GM in dynamic operating conditions

Step towards secure and reliable smart grids in Industry 5.0: A federated learning assisted hybrid deep learning model for electricity theft detection using smart meters

The integration of Smart Grid technology and conceptual Industry 5.0 has paved the way for advanced energy management systems that enhance efficiency and revolutionized the parallel integration of power sources in a sustainable manner. However, this digitization has opened a new stream of the threat and opportunities of electricity theft posing a significant challenge to the security and reliability of Smart Grid networks. In this paper, we propose a secure and reliable theft detection technique using deep federated learning (FL) mechanism. The technique leverages the collaborative power of FL to train a Convolutional Gated Recurrent Unit (ConvGRU) model on distributed data sources without compromising data privacy. The training deep learning model backbone consists of a ConvGRU model that combines convolutional and gated recurrent units to capture spatial and temporal patterns in electricity consumption data. An improvised preprocessing mechanism and hyperparameter tuning is done to facilitate FL mechanism. The halving randomized search algorithm is used for hyperparameters tuning of the ConvGRU model. The impact of hyperparameters involved in the ConvGRU model such as number of layers, filters, kernel size, activation function, pooling, GRU layers, hidden state dimension, learning rate, and the dropout rate is elaborated. The proposed technique achieves promising results, with high accuracy, precision, recall, and F1 score, demonstrating its efficacy in detecting electricity theft in Smart Grid networks. Comparative analysis with existing techniques reveal the superior performance of the deep FL-based ConvGRU model. The findings highlight the potential of this approach in enhancing the security and efficiency of Smart Grid systems while preserving data privacy